The proposed detection engine uses an autoencoder model based on
sequence to sequence architecture that is made up of LSTM (Long
Short Term Memory) [1]} cells. LSTM networks are complex
neural networks that are used to train ordered sequences of inputs
to remember it and re-create it. The proposed model devises the
LSTM neural network model, which feeds sequenced inputs. After
completing the reading input processes, the output is given by an
internal learned representation of the fed input sequences as a
fixed-length vector. Then, output vector is fed inputs that
interprets the input sequence by sequence at each step and the
output is generated.
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